1. Field of the Invention
This invention relates generally to mobile device graphical interface technologies and, more specifically, to controlling inadvertent inputs to an input area of a mobile device.
2. Description of the Related Art
A touchscreen interface is an electronic visual display that the user can control through simple or multi-touch gestures by touching the screen with one or more fingers. Some touchscreens can also detect objects such as a stylus or specially coated gloves. The user can use the touchscreen to react to what is displayed and to control how it is displayed. The touchscreen enables the user to interact directly with what is displayed, rather than using a mouse, touchpad, or any other intermediate device. Touchscreens are common in devices such as game consoles, all-in-one computers, tablet computers, and smartphones. They also play a prominent role in the design of digital appliances such as personal digital assistants (PDAs), satellite navigation devices, mobile phones, and video games. The popularity of smartphones, tablets, and many types of information appliances is driving the demand and acceptance of common touchscreens for portable and functional electronics.
With capacitive touchscreen technology, typically only one side of the insulator is coated with conductive material. A small voltage is applied to this layer, resulting in a uniform electrostatic field. When a conductor, such as a human finger, touches the uncoated surface, a capacitor is dynamically formed. Because of the sheet resistance of the surface, each corner is measured to have a different effective capacitance. The sensor's controller can determine the location of the touch indirectly from the change in the capacitance as measured from the four corners of the panel. Typically, the larger the change in capacitance, the closer the touch is to that corner. With no moving parts, it is moderately durable, but has low resolution, is prone to false signals from parasitic capacitive coupling, and needs calibration during manufacture.
Gesture interfaces based on inertial sensors such as accelerometers and gyroscopes embedded in small form factor electronic devices are becoming increasingly common in user devices such as smart phones, remote controllers and game consoles. In the mobile device space, gesture interaction is an attractive alternative to traditional interfaces because it does not involve the shrinking of the form factor of traditional input devices such as a keyboard, mouse or screen. In addition, gesture interaction is more supportive of mobility, as users can easily perform subtle gestures as they perform other activities.
“Dynamic 3D gestures” are based on atomic movements of a user using inertial sensors such as micro-electromechanical system (MEMS) based accelerometers and gyroscopes. Statistical recognition algorithms, such as Hidden Markov Model algorithms (HMM), are used for gesture and speech recognition and many other machine learning tasks. HMM is effective for recognizing complex gestures and enabling rich gesture input vocabularies. However, due to the nature of statistical algorithms, including the necessary feature extraction and normalization employed to deal with gesture-to-gesture and user-to-user variability, these algorithms often suffer from a high rate of false positives that negatively impact the performance of the system and the user experience.